





'''simple docstring'''






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer
					class 		lowerCamelCase	(   _A							):



						def __init__( self		,   a_ = 101						):
								lowerCAmelCase  :     Dict       						= length



						def __len__( self						):
								return self.length





						def __getitem__( self		,   a_						):
								return i






					class 		lowerCamelCase	:



						def __call__( self		,   a_						):
								return {"input_ids": torch.tensor(a_						), "labels": torch.tensor(a_						)}






					class 		lowerCamelCase	(   nn.Module							):



						def __init__( self						):
								super().__init__()
								# Add some (unused) params otherwise DDP will complain.
								lowerCAmelCase  :     Optional[int]       						= nn.Linear(120		,   80						)





						def 	_lowerCamelCase     ( self		,   a_		,   a_=None						):
								if labels is not None:
										return torch.tensor(0.0		,   device=input_ids.device						), input_ids
								else:
										return input_ids
class 		lowerCamelCase	(   _A							):



	@require_torch_neuroncore
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
			lowerCAmelCase  :     Optional[Any]       						= self.get_auto_remove_tmp_dir()
			lowerCAmelCase  :     Dict       						= F'''--output_dir {output_dir}'''.split()
			lowerCAmelCase  :     Dict       						= ["torchrun"] + distributed_args + args
			execute_subprocess_async(a_		,   env=self.get_env()						)
			# successful return here == success - any errors would have caused an error in the sub-call






class 		lowerCamelCase	(   _A							):



	@require_torch_multi_gpu
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
			lowerCAmelCase  :     List[str]       						= self.get_auto_remove_tmp_dir()
			lowerCAmelCase  :     Any       						= F'''--output_dir {output_dir}'''.split()
			lowerCAmelCase  :     List[Any]       						= ["torchrun"] + distributed_args + args
			execute_subprocess_async(a_		,   env=self.get_env()						)
			# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					lowerCAmelCase									=   HfArgumentParser((TrainingArguments,))
					lowerCAmelCase									=   parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
					    F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [1_01, 40, 7]:
										lowerCAmelCase									=   DummyDataset(dataset_length)






										def   __A    (				a_    :	EvalPrediction						):
												lowerCAmelCase  :     Optional[int]       						= list(range(len(a_						)						)						)
												lowerCAmelCase  :     List[Any]       						= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
												if not success and training_args.local_rank == 0:
														logger.warning(
														    "Predictions and/or labels do not match expected results:\n  - predictions: "
														    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''						)
												return {"success": success}

										lowerCAmelCase									=   Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										lowerCAmelCase									=   trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										lowerCAmelCase									=   trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										lowerCAmelCase									=   2

										lowerCAmelCase									=   trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										lowerCAmelCase									=   trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										lowerCAmelCase									=   None










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



